Technologies for managing errors in a remotely accessible memory pool

ABSTRACT

Technologies for managing errors in a remotely accessible memory pool include a memory sled. The memory sled includes a memory pool having one or more byte-addressable memory devices and a memory pool controller coupled to the memory pool. The memory sled is to write test data to a byte-addressable memory region in the memory pool. The memory region is to be accessed by a remote compute sled. The memory sled is also to read data from the memory region to which the test data was written, compare the read data to the test data to determine whether a threshold number of errors are present in the read data, and send, in response to a determination that the threshold number of errors are present in the read data, a notification to the remote compute sled that the memory region is faulty.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims the benefit of Indian Provisional PatentApplication No. 201741030632, filed Aug. 30, 2017 and U.S. ProvisionalPatent Application No. 62/584,401, filed Nov. 10, 2017.

BACKGROUND

Typical data centers do not provide a pool of byte-addressable memory(e.g., RAM, non-volatile memory), to be accessed by other computedevices on an as requested basis. Rather, such memory is installed intoeach separate compute device, which can lead to over-provisioning oflocal memory in each compute device if the compute devices typically donot use all of the memory that they are equipped with. Further, insystems that provide access to remotely accessible resources, thecondition (e.g., health) of the underlying hardware of those resourcesis often unknown to the compute device that is utilizing the resource.As such, the compute device may only be notified of an issue with thecondition of the remote hardware when the hardware fails completely,potentially resulting in loss of data.

BRIEF DESCRIPTION OF THE DRAWINGS

The concepts described herein are illustrated by way of example and notby way of limitation in the accompanying figures. For simplicity andclarity of illustration, elements illustrated in the figures are notnecessarily drawn to scale. Where considered appropriate, referencelabels have been repeated among the figures to indicate corresponding oranalogous elements.

FIG. 1 is a simplified diagram of at least one embodiment of a datacenter for executing workloads with disaggregated resources;

FIG. 2 is a simplified diagram of at least one embodiment of a pod ofthe data center of FIG. 1;

FIG. 3 is a perspective view of at least one embodiment of a rack thatmay be included in the pod of FIG. 2;

FIG. 4 is a side plan elevation view of the rack of FIG. 3;

FIG. 5 is a perspective view of the rack of FIG. 3 having a sled mountedtherein;

FIG. 6 is a is a simplified block diagram of at least one embodiment ofa top side of the sled of FIG. 5;

FIG. 7 is a simplified block diagram of at least one embodiment of abottom side of the sled of FIG. 6;

FIG. 8 is a simplified block diagram of at least one embodiment of acompute sled usable in the data center of FIG. 1;

FIG. 9 is a top perspective view of at least one embodiment of thecompute sled of FIG. 8;

FIG. 10 is a simplified block diagram of at least one embodiment of anaccelerator sled usable in the data center of FIG. 1;

FIG. 11 is a top perspective view of at least one embodiment of theaccelerator sled of FIG. 10;

FIG. 12 is a simplified block diagram of at least one embodiment of astorage sled usable in the data center of FIG. 1;

FIG. 13 is a top perspective view of at least one embodiment of thestorage sled of FIG. 12;

FIG. 14 is a simplified block diagram of at least one embodiment of amemory sled usable in the data center of FIG. 1; and

FIG. 15 is a simplified block diagram of a system that may beestablished within the data center of FIG. 1 to execute workloads withmanaged nodes composed of disaggregated resources.

FIG. 16 is a simplified block diagram of at least one embodiment of asystem for managing errors in a remotely accessible memory pool;

FIG. 17 is a simplified block diagram of at least one embodiment of amemory sled of the system of FIG. 16;

FIG. 18 is a simplified block diagram of at least one embodiment of anenvironment that may be established by the memory sled of FIGS. 16 and17; and

FIGS. 19-20 are a simplified flow diagram of at least one embodiment ofa method for managing errors in a memory pool that may be performed bythe memory sled of FIGS. 16 and 17.

DETAILED DESCRIPTION OF THE DRAWINGS

While the concepts of the present disclosure are susceptible to variousmodifications and alternative forms, specific embodiments thereof havebeen shown by way of example in the drawings and will be describedherein in detail. It should be understood, however, that there is nointent to limit the concepts of the present disclosure to the particularforms disclosed, but on the contrary, the intention is to cover allmodifications, equivalents, and alternatives consistent with the presentdisclosure and the appended claims.

References in the specification to “one embodiment,” “an embodiment,”“an illustrative embodiment,” etc., indicate that the embodimentdescribed may include a particular feature, structure, orcharacteristic, but every embodiment may or may not necessarily includethat particular feature, structure, or characteristic. Moreover, suchphrases are not necessarily referring to the same embodiment. Further,when a particular feature, structure, or characteristic is described inconnection with an embodiment, it is submitted that it is within theknowledge of one skilled in the art to effect such feature, structure,or characteristic in connection with other embodiments whether or notexplicitly described. Additionally, it should be appreciated that itemsincluded in a list in the form of “at least one A, B, and C” can mean(A); (B); (C); (A and B); (A and C); (B and C); or (A, B, and C).Similarly, items listed in the form of “at least one of A, B, or C” canmean (A); (B); (C); (A and B); (A and C); (B and C); or (A, B, and C).

The disclosed embodiments may be implemented, in some cases, inhardware, firmware, software, or any combination thereof. The disclosedembodiments may also be implemented as instructions carried by or storedon a transitory or non-transitory machine-readable (e.g.,computer-readable) storage medium, which may be read and executed by oneor more processors. A machine-readable storage medium may be embodied asany storage device, mechanism, or other physical structure for storingor transmitting information in a form readable by a machine (e.g., avolatile or non-volatile memory, a media disc, or other media device).

In the drawings, some structural or method features may be shown inspecific arrangements and/or orderings. However, it should beappreciated that such specific arrangements and/or orderings may not berequired. Rather, in some embodiments, such features may be arranged ina different manner and/or order than shown in the illustrative figures.Additionally, the inclusion of a structural or method feature in aparticular figure is not meant to imply that such feature is required inall embodiments and, in some embodiments, may not be included or may becombined with other features.

Referring now to FIG. 1, a data center 100 in which disaggregatedresources may cooperatively execute one or more workloads (e.g.,applications on behalf of customers) includes multiple pods 110, 120,130, 140, each of which includes one or more rows of racks. As describedin more detail herein, each rack houses multiple sleds, which each maybe embodied as a compute device, such as a server, that is primarilyequipped with a particular type of resource (e.g., memory devices, datastorage devices, accelerator devices, general purpose processors). Inthe illustrative embodiment, the sleds in each pod 110, 120, 130, 140are connected to multiple pod switches (e.g., switches that route datacommunications to and from sleds within the pod). The pod switches, inturn, connect with spine switches 150 that switch communications amongpods (e.g., the pods 110, 120, 130, 140) in the data center 100. In someembodiments, the sleds may be connected with a fabric using IntelOmni-Path technology. As described in more detail herein, resourceswithin sleds in the data center 100 may be allocated to a group(referred to herein as a “managed node”) containing resources from oneor more other sleds to be collectively utilized in the execution of aworkload. The workload can execute as if the resources belonging to themanaged node were located on the same sled. The resources in a managednode may even belong to sleds belonging to different racks, and even todifferent pods 110, 120, 130, 140. Some resources of a single sled maybe allocated to one managed node while other resources of the same sledare allocated to a different managed node (e.g., one processor assignedto one managed node and another processor of the same sled assigned to adifferent managed node). By disaggregating resources to sleds comprisedpredominantly of a single type of resource (e.g., compute sledscomprising primarily compute resources, memory sleds containingprimarily memory resources), and selectively allocating and deallocatingthe disaggregated resources to form a managed node assigned to execute aworkload, the data center 100 provides more efficient resource usageover typical data centers comprised of hyperconverged servers containingcompute, memory, storage and perhaps additional resources). As such, thedata center 100 may provide greater performance (e.g., throughput,operations per second, latency, etc.) than a typical data center thathas the same number of resources.

Referring now to FIG. 2, the pod 110, in the illustrative embodiment,includes a set of rows 200, 210, 220, 230 of racks 240. Each rack 240may house multiple sleds (e.g., sixteen sleds) and provide power anddata connections to the housed sleds, as described in more detailherein. In the illustrative embodiment, the racks in each row 200, 210,220, 230 are connected to multiple pod switches 250, 260. The pod switch250 includes a set of ports 252 to which the sleds of the racks of thepod 110 are connected and another set of ports 254 that connect the pod110 to the spine switches 150 to provide connectivity to other pods inthe data center 100. Similarly, the pod switch 260 includes a set ofports 262 to which the sleds of the racks of the pod 110 are connectedand a set of ports 264 that connect the pod 110 to the spine switches150. As such, the use of the pair of switches 250, 260 provides anamount of redundancy to the pod 110. For example, if either of theswitches 250, 260 fails, the sleds in the pod 110 may still maintaindata communication with the remainder of the data center 100 (e.g.,sleds of other pods) through the other switch 250, 260. Furthermore, inthe illustrative embodiment, the switches 150, 250, 260 may be embodiedas dual-mode optical switches, capable of routing both Ethernet protocolcommunications carrying Internet Protocol (IP) packets andcommunications according to a second, high-performance link-layerprotocol (e.g., Intel's Omni-Path Architecture's, Infiniband) viaoptical signaling media of an optical fabric.

It should be appreciated that each of the other pods 120, 130, 140 (aswell as any additional pods of the data center 100) may be similarlystructured as, and have components similar to, the pod 110 shown in anddescribed in regard to FIG. 2 (e.g., each pod may have rows of rackshousing multiple sleds as described above). Additionally, while two podswitches 250, 260 are shown, it should be understood that in otherembodiments, each pod 110, 120, 130, 140 may be connected to differentnumber of pod switches (e.g., providing even more failover capacity).

Referring now to FIGS. 3-5, each illustrative rack 240 of the datacenter 100 includes two elongated support posts 302, 304, which arearranged vertically. For example, the elongated support posts 302, 304may extend upwardly from a floor of the data center 100 when deployed.The rack 240 also includes one or more horizontal pairs 310 of elongatedsupport arms 312 (identified in FIG. 3 via a dashed ellipse) configuredto support a sled of the data center 100 as discussed below. Oneelongated support arm 312 of the pair of elongated support arms 312extends outwardly from the elongated support post 302 and the otherelongated support arm 312 extends outwardly from the elongated supportpost 304.

In the illustrative embodiments, each sled of the data center 100 isembodied as a chassis-less sled. That is, each sled has a chassis-lesscircuit board substrate on which physical resources (e.g., processors,memory, accelerators, storage, etc.) are mounted as discussed in moredetail below. As such, the rack 240 is configured to receive thechassis-less sleds. For example, each pair 310 of elongated support arms312 defines a sled slot 320 of the rack 240, which is configured toreceive a corresponding chassis-less sled. To do so, each illustrativeelongated support arm 312 includes a circuit board guide 330 configuredto receive the chassis-less circuit board substrate of the sled. Eachcircuit board guide 330 is secured to, or otherwise mounted to, a topside 332 of the corresponding elongated support arm 312. For example, inthe illustrative embodiment, each circuit board guide 330 is mounted ata distal end of the corresponding elongated support arm 312 relative tothe corresponding elongated support post 302, 304. For clarity of theFigures, not every circuit board guide 330 may be referenced in eachFigure.

Each circuit board guide 330 includes an inner wall that defines acircuit board slot 380 configured to receive the chassis-less circuitboard substrate of a sled 400 when the sled 400 is received in thecorresponding sled slot 320 of the rack 240. To do so, as shown in FIG.4, a user (or robot) aligns the chassis-less circuit board substrate ofan illustrative chassis-less sled 400 to a sled slot 320. The user, orrobot, may then slide the chassis-less circuit board substrate forwardinto the sled slot 320 such that each side edge 414 of the chassis-lesscircuit board substrate is received in a corresponding circuit boardslot 380 of the circuit board guides 330 of the pair 310 of elongatedsupport arms 312 that define the corresponding sled slot 320 as shown inFIG. 4. By having robotically accessible and robotically manipulablesleds comprising disaggregated resources, each type of resource can beupgraded independently of each other and at their own optimized refreshrate. Furthermore, the sleds are configured to blindly mate with powerand data communication cables in each rack 240, enhancing their abilityto be quickly removed, upgraded, reinstalled, and/or replaced. As such,in some embodiments, the data center 100 may operate (e.g., executeworkloads, undergo maintenance and/or upgrades, etc.) without humaninvolvement on the data center floor. In other embodiments, a human mayfacilitate one or more maintenance or upgrade operations in the datacenter 100.

It should be appreciated that each circuit board guide 330 is dualsided. That is, each circuit board guide 330 includes an inner wall thatdefines a circuit board slot 380 on each side of the circuit board guide330. In this way, each circuit board guide 330 can support achassis-less circuit board substrate on either side. As such, a singleadditional elongated support post may be added to the rack 240 to turnthe rack 240 into a two-rack solution that can hold twice as many sledslots 320 as shown in FIG. 3. The illustrative rack 240 includes sevenpairs 310 of elongated support arms 312 that define a correspondingseven sled slots 320, each configured to receive and support acorresponding sled 400 as discussed above. Of course, in otherembodiments, the rack 240 may include additional or fewer pairs 310 ofelongated support arms 312 (i.e., additional or fewer sled slots 320).It should be appreciated that because the sled 400 is chassis-less, thesled 400 may have an overall height that is different than typicalservers. As such, in some embodiments, the height of each sled slot 320may be shorter than the height of a typical server (e.g., shorter than asingle rank unit, “1 U”). That is, the vertical distance between eachpair 310 of elongated support arms 312 may be less than a standard rackunit “1 U.” Additionally, due to the relative decrease in height of thesled slots 320, the overall height of the rack 240 in some embodimentsmay be shorter than the height of traditional rack enclosures. Forexample, in some embodiments, each of the elongated support posts 302,304 may have a length of six feet or less. Again, in other embodiments,the rack 240 may have different dimensions. Further, it should beappreciated that the rack 240 does not include any walls, enclosures, orthe like. Rather, the rack 240 is an enclosure-less rack that is openedto the local environment. Of course, in some cases, an end plate may beattached to one of the elongated support posts 302, 304 in thosesituations in which the rack 240 forms an end-of-row rack in the datacenter 100.

In some embodiments, various interconnects may be routed upwardly ordownwardly through the elongated support posts 302, 304. To facilitatesuch routing, each elongated support post 302, 304 includes an innerwall that defines an inner chamber in which the interconnect may belocated. The interconnects routed through the elongated support posts302, 304 may be embodied as any type of interconnects including, but notlimited to, data or communication interconnects to provide communicationconnections to each sled slot 320, power interconnects to provide powerto each sled slot 320, and/or other types of interconnects.

The rack 240, in the illustrative embodiment, includes a supportplatform on which a corresponding optical data connector (not shown) ismounted. Each optical data connector is associated with a correspondingsled slot 320 and is configured to mate with an optical data connectorof a corresponding sled 400 when the sled 400 is received in thecorresponding sled slot 320. In some embodiments, optical connectionsbetween components (e.g., sleds, racks, and switches) in the data center100 are made with a blind mate optical connection. For example, a dooron each cable may prevent dust from contaminating the fiber inside thecable. In the process of connecting to a blind mate optical connectormechanism, the door is pushed open when the end of the cable enters theconnector mechanism. Subsequently, the optical fiber inside the cableenters a gel within the connector mechanism and the optical fiber of onecable comes into contact with the optical fiber of another cable withinthe gel inside the connector mechanism.

The illustrative rack 240 also includes a fan array 370 coupled to thecross-support arms of the rack 240. The fan array 370 includes one ormore rows of cooling fans 372, which are aligned in a horizontal linebetween the elongated support posts 302, 304. In the illustrativeembodiment, the fan array 370 includes a row of cooling fans 372 foreach sled slot 320 of the rack 240. As discussed above, each sled 400does not include any on-board cooling system in the illustrativeembodiment and, as such, the fan array 370 provides cooling for eachsled 400 received in the rack 240. Each rack 240, in the illustrativeembodiment, also includes a power supply associated with each sled slot320. Each power supply is secured to one of the elongated support arms312 of the pair 310 of elongated support arms 312 that define thecorresponding sled slot 320. For example, the rack 240 may include apower supply coupled or secured to each elongated support arm 312extending from the elongated support post 302. Each power supplyincludes a power connector configured to mate with a power connector ofthe sled 400 when the sled 400 is received in the corresponding sledslot 320. In the illustrative embodiment, the sled 400 does not includeany on-board power supply and, as such, the power supplies provided inthe rack 240 supply power to corresponding sleds 400 when mounted to therack 240.

Referring now to FIG. 6, the sled 400, in the illustrative embodiment,is configured to be mounted in a corresponding rack 240 of the datacenter 100 as discussed above. In some embodiments, each sled 400 may beoptimized or otherwise configured for performing particular tasks, suchas compute tasks, acceleration tasks, data storage tasks, etc. Forexample, the sled 400 may be embodied as a compute sled 800 as discussedbelow in regard to FIGS. 8-9, an accelerator sled 1000 as discussedbelow in regard to FIGS. 10-11, a storage sled 1200 as discussed belowin regard to FIGS. 12-13, or as a sled optimized or otherwise configuredto perform other specialized tasks, such as a memory sled 1400,discussed below in regard to FIG. 14.

As discussed above, the illustrative sled 400 includes a chassis-lesscircuit board substrate 602, which supports various physical resources(e.g., electrical components) mounted thereon. It should be appreciatedthat the circuit board substrate 602 is “chassis-less” in that the sled400 does not include a housing or enclosure. Rather, the chassis-lesscircuit board substrate 602 is open to the local environment. Thechassis-less circuit board substrate 602 may be formed from any materialcapable of supporting the various electrical components mounted thereon.For example, in an illustrative embodiment, the chassis-less circuitboard substrate 602 is formed from an FR-4 glass-reinforced epoxylaminate material. Of course, other materials may be used to form thechassis-less circuit board substrate 602 in other embodiments.

As discussed in more detail below, the chassis-less circuit boardsubstrate 602 includes multiple features that improve the thermalcooling characteristics of the various electrical components mounted onthe chassis-less circuit board substrate 602. As discussed, thechassis-less circuit board substrate 602 does not include a housing orenclosure, which may improve the airflow over the electrical componentsof the sled 400 by reducing those structures that may inhibit air flow.For example, because the chassis-less circuit board substrate 602 is notpositioned in an individual housing or enclosure, there is no backplane(e.g., a backplate of the chassis) to the chassis-less circuit boardsubstrate 602, which could inhibit air flow across the electricalcomponents. Additionally, the chassis-less circuit board substrate 602has a geometric shape configured to reduce the length of the airflowpath across the electrical components mounted to the chassis-lesscircuit board substrate 602. For example, the illustrative chassis-lesscircuit board substrate 602 has a width 604 that is greater than a depth606 of the chassis-less circuit board substrate 602. In one particularembodiment, for example, the chassis-less circuit board substrate 602has a width of about 21 inches and a depth of about 9 inches, comparedto a typical server that has a width of about 17 inches and a depth ofabout 39 inches. As such, an airflow path 608 that extends from a frontedge 610 of the chassis-less circuit board substrate 602 toward a rearedge 612 has a shorter distance relative to typical servers, which mayimprove the thermal cooling characteristics of the sled 400.Furthermore, although not illustrated in FIG. 6, the various physicalresources mounted to the chassis-less circuit board substrate 602 aremounted in corresponding locations such that no two substantivelyheat-producing electrical components shadow each other as discussed inmore detail below. That is, no two electrical components, which produceappreciable heat during operation (i.e., greater than a nominal heatsufficient enough to adversely impact the cooling of another electricalcomponent), are mounted to the chassis-less circuit board substrate 602linearly in-line with each other along the direction of the airflow path608 (i.e., along a direction extending from the front edge 610 towardthe rear edge 612 of the chassis-less circuit board substrate 602).

As discussed above, the illustrative sled 400 includes one or morephysical resources 620 mounted to a top side 650 of the chassis-lesscircuit board substrate 602. Although two physical resources 620 areshown in FIG. 6, it should be appreciated that the sled 400 may includeone, two, or more physical resources 620 in other embodiments. Thephysical resources 620 may be embodied as any type of processor,controller, or other compute circuit capable of performing various taskssuch as compute functions and/or controlling the functions of the sled400 depending on, for example, the type or intended functionality of thesled 400. For example, as discussed in more detail below, the physicalresources 620 may be embodied as high-performance processors inembodiments in which the sled 400 is embodied as a compute sled, asaccelerator co-processors or circuits in embodiments in which the sled400 is embodied as an accelerator sled, storage controllers inembodiments in which the sled 400 is embodied as a storage sled, or aset of memory devices in embodiments in which the sled 400 is embodiedas a memory sled.

The sled 400 also includes one or more additional physical resources 630mounted to the top side 650 of the chassis-less circuit board substrate602. In the illustrative embodiment, the additional physical resourcesinclude a network interface controller (NIC) as discussed in more detailbelow. Of course, depending on the type and functionality of the sled400, the physical resources 630 may include additional or otherelectrical components, circuits, and/or devices in other embodiments.

The physical resources 620 are communicatively coupled to the physicalresources 630 via an input/output (I/O) subsystem 622. The I/O subsystem622 may be embodied as circuitry and/or components to facilitateinput/output operations with the physical resources 620, the physicalresources 630, and/or other components of the sled 400. For example, theI/O subsystem 622 may be embodied as, or otherwise include, memorycontroller hubs, input/output control hubs, integrated sensor hubs,firmware devices, communication links (e.g., point-to-point links, buslinks, wires, cables, light guides, printed circuit board traces, etc.),and/or other components and subsystems to facilitate the input/outputoperations. In the illustrative embodiment, the I/O subsystem 622 isembodied as, or otherwise includes, a double data rate 4 (DDR4) data busor a DDR5 data bus.

In some embodiments, the sled 400 may also include aresource-to-resource interconnect 624. The resource-to-resourceinterconnect 624 may be embodied as any type of communicationinterconnect capable of facilitating resource-to-resourcecommunications. In the illustrative embodiment, the resource-to-resourceinterconnect 624 is embodied as a high-speed point-to-point interconnect(e.g., faster than the I/O subsystem 622). For example, theresource-to-resource interconnect 624 may be embodied as a QuickPathInterconnect (QPI), an UltraPath Interconnect (UPI), or other high-speedpoint-to-point interconnect dedicated to resource-to-resourcecommunications.

The sled 400 also includes a power connector 640 configured to mate witha corresponding power connector of the rack 240 when the sled 400 ismounted in the corresponding rack 240. The sled 400 receives power froma power supply of the rack 240 via the power connector 640 to supplypower to the various electrical components of the sled 400. That is, thesled 400 does not include any local power supply (i.e., an on-boardpower supply) to provide power to the electrical components of the sled400. The exclusion of a local or on-board power supply facilitates thereduction in the overall footprint of the chassis-less circuit boardsubstrate 602, which may increase the thermal cooling characteristics ofthe various electrical components mounted on the chassis-less circuitboard substrate 602 as discussed above. In some embodiments, power isprovided to the processors 820 through vias directly under theprocessors 820 (e.g., through the bottom side 750 of the chassis-lesscircuit board substrate 602), providing an increased thermal budget,additional current and/or voltage, and better voltage control overtypical boards.

In some embodiments, the sled 400 may also include mounting features 642configured to mate with a mounting arm, or other structure, of a robotto facilitate the placement of the sled 600 in a rack 240 by the robot.The mounting features 642 may be embodied as any type of physicalstructures that allow the robot to grasp the sled 400 without damagingthe chassis-less circuit board substrate 602 or the electricalcomponents mounted thereto. For example, in some embodiments, themounting features 642 may be embodied as non-conductive pads attached tothe chassis-less circuit board substrate 602. In other embodiments, themounting features may be embodied as brackets, braces, or other similarstructures attached to the chassis-less circuit board substrate 602. Theparticular number, shape, size, and/or make-up of the mounting feature642 may depend on the design of the robot configured to manage the sled400.

Referring now to FIG. 7, in addition to the physical resources 630mounted on the top side 650 of the chassis-less circuit board substrate602, the sled 400 also includes one or more memory devices 720 mountedto a bottom side 750 of the chassis-less circuit board substrate 602.That is, the chassis-less circuit board substrate 602 is embodied as adouble-sided circuit board. The physical resources 620 arecommunicatively coupled to the memory devices 720 via the I/O subsystem622. For example, the physical resources 620 and the memory devices 720may be communicatively coupled by one or more vias extending through thechassis-less circuit board substrate 602. Each physical resource 620 maybe communicatively coupled to a different set of one or more memorydevices 720 in some embodiments. Alternatively, in other embodiments,each physical resource 620 may be communicatively coupled to each memorydevices 720.

The memory devices 720 may be embodied as any type of memory devicecapable of storing data for the physical resources 620 during operationof the sled 400, such as any type of volatile (e.g., dynamic randomaccess memory (DRAM), etc.) or non-volatile memory. Volatile memory maybe a storage medium that requires power to maintain the state of datastored by the medium. Non-limiting examples of volatile memory mayinclude various types of random access memory (RAM), such as dynamicrandom access memory (DRAM) or static random access memory (SRAM). Oneparticular type of DRAM that may be used in a memory module issynchronous dynamic random access memory (SDRAM). In particularembodiments, DRAM of a memory component may comply with a standardpromulgated by JEDEC, such as JESD79F for DDR SDRAM, JESD79-2F for DDR2SDRAM, JESD79-3F for DDR3 SDRAM, JESD79-4A for DDR4 SDRAM, JESD209 forLow Power DDR (LPDDR), JESD209-2 for LPDDR2, JESD209-3 for LPDDR3, andJESD209-4 for LPDDR4 (these standards are available at www.jedec.org).Such standards (and similar standards) may be referred to as DDR-basedstandards and communication interfaces of the storage devices thatimplement such standards may be referred to as DDR-based interfaces.

In one embodiment, the memory device is a block addressable memorydevice, such as those based on NAND or NOR technologies. A memory devicemay also include next-generation nonvolatile devices, such as Intel 3DXPoint™ memory or other byte addressable write-in-place nonvolatilememory devices. In one embodiment, the memory device may be or mayinclude memory devices that use chalcogenide glass, multi-thresholdlevel NAND flash memory, NOR flash memory, single or multi-level PhaseChange Memory (PCM), a resistive memory, nanowire memory, ferroelectrictransistor random access memory (FeTRAM), anti-ferroelectric memory,magnetoresistive random access memory (MRAM) memory that incorporatesmemristor technology, resistive memory including the metal oxide base,the oxygen vacancy base and the conductive bridge Random Access Memory(CB-RAM), or spin transfer torque (STT)-MRAM, a spintronic magneticjunction memory based device, a magnetic tunneling junction (MTJ) baseddevice, a DW (Domain Wall) and SOT (Spin Orbit Transfer) based device, athyristor based memory device, or a combination of any of the above, orother memory. The memory device may refer to the die itself and/or to apackaged memory product. In some embodiments, the memory device maycomprise a transistor-less stackable cross point architecture in whichmemory cells sit at the intersection of word lines and bit lines and areindividually addressable and in which bit storage is based on a changein bulk resistance.

Referring now to FIG. 8, in some embodiments, the sled 400 may beembodied as a compute sled 800. The compute sled 800 is optimized, orotherwise configured, to perform compute tasks. Of course, as discussedabove, the compute sled 800 may rely on other sleds, such asacceleration sleds and/or storage sleds, to perform such compute tasks.The compute sled 800 includes various physical resources (e.g.,electrical components) similar to the physical resources of the sled400, which have been identified in FIG. 8 using the same referencenumbers. The description of such components provided above in regard toFIGS. 6 and 7 applies to the corresponding components of the computesled 800 and is not repeated herein for clarity of the description ofthe compute sled 800.

In the illustrative compute sled 800, the physical resources 620 areembodied as processors 820. Although only two processors 820 are shownin FIG. 8, it should be appreciated that the compute sled 800 mayinclude additional processors 820 in other embodiments. Illustratively,the processors 820 are embodied as high-performance processors 820 andmay be configured to operate at a relatively high power rating. Althoughthe processors 820 generate additional heat operating at power ratingsgreater than typical processors (which operate at around 155-230 W), theenhanced thermal cooling characteristics of the chassis-less circuitboard substrate 602 discussed above facilitate the higher poweroperation. For example, in the illustrative embodiment, the processors820 are configured to operate at a power rating of at least 250 W. Insome embodiments, the processors 820 may be configured to operate at apower rating of at least 350 W.

In some embodiments, the compute sled 800 may also include aprocessor-to-processor interconnect 842. Similar to theresource-to-resource interconnect 624 of the sled 400 discussed above,the processor-to-processor interconnect 842 may be embodied as any typeof communication interconnect capable of facilitatingprocessor-to-processor interconnect 842 communications. In theillustrative embodiment, the processor-to-processor interconnect 842 isembodied as a high-speed point-to-point interconnect (e.g., faster thanthe I/O subsystem 622). For example, the processor-to-processorinterconnect 842 may be embodied as a QuickPath Interconnect (QPI), anUltraPath Interconnect (UPI), or other high-speed point-to-pointinterconnect dedicated to processor-to-processor communications.

The compute sled 800 also includes a communication circuit 830. Theillustrative communication circuit 830 includes a network interfacecontroller (NIC) 832, which may also be referred to as a host fabricinterface (HFI). The NIC 832 may be embodied as, or otherwise include,any type of integrated circuit, discrete circuits, controller chips,chipsets, add-in-boards, daughtercards, network interface cards, otherdevices that may be used by the compute sled 800 to connect with anothercompute device (e.g., with other sleds 400). In some embodiments, theNIC 832 may be embodied as part of a system-on-a-chip (SoC) thatincludes one or more processors, or included on a multichip package thatalso contains one or more processors. In some embodiments, the NIC 832may include a local processor (not shown) and/or a local memory (notshown) that are both local to the NIC 832. In such embodiments, thelocal processor of the NIC 832 may be capable of performing one or moreof the functions of the processors 820. Additionally or alternatively,in such embodiments, the local memory of the NIC 832 may be integratedinto one or more components of the compute sled at the board level,socket level, chip level, and/or other levels.

The communication circuit 830 is communicatively coupled to an opticaldata connector 834. The optical data connector 834 is configured to matewith a corresponding optical data connector of the rack 240 when thecompute sled 800 is mounted in the rack 240. Illustratively, the opticaldata connector 834 includes a plurality of optical fibers which leadfrom a mating surface of the optical data connector 834 to an opticaltransceiver 836. The optical transceiver 836 is configured to convertincoming optical signals from the rack-side optical data connector toelectrical signals and to convert electrical signals to outgoing opticalsignals to the rack-side optical data connector. Although shown asforming part of the optical data connector 834 in the illustrativeembodiment, the optical transceiver 836 may form a portion of thecommunication circuit 830 in other embodiments.

In some embodiments, the compute sled 800 may also include an expansionconnector 840. In such embodiments, the expansion connector 840 isconfigured to mate with a corresponding connector of an expansionchassis-less circuit board substrate to provide additional physicalresources to the compute sled 800. The additional physical resources maybe used, for example, by the processors 820 during operation of thecompute sled 800. The expansion chassis-less circuit board substrate maybe substantially similar to the chassis-less circuit board substrate 602discussed above and may include various electrical components mountedthereto. The particular electrical components mounted to the expansionchassis-less circuit board substrate may depend on the intendedfunctionality of the expansion chassis-less circuit board substrate. Forexample, the expansion chassis-less circuit board substrate may provideadditional compute resources, memory resources, and/or storageresources. As such, the additional physical resources of the expansionchassis-less circuit board substrate may include, but is not limited to,processors, memory devices, storage devices, and/or accelerator circuitsincluding, for example, field programmable gate arrays (FPGA),application-specific integrated circuits (ASICs), securityco-processors, graphics processing units (GPUs), machine learningcircuits, or other specialized processors, controllers, devices, and/orcircuits.

Referring now to FIG. 9, an illustrative embodiment of the compute sled800 is shown. As shown, the processors 820, communication circuit 830,and optical data connector 834 are mounted to the top side 650 of thechassis-less circuit board substrate 602. Any suitable attachment ormounting technology may be used to mount the physical resources of thecompute sled 800 to the chassis-less circuit board substrate 602. Forexample, the various physical resources may be mounted in correspondingsockets (e.g., a processor socket), holders, or brackets. In some cases,some of the electrical components may be directly mounted to thechassis-less circuit board substrate 602 via soldering or similartechniques.

As discussed above, the individual processors 820 and communicationcircuit 830 are mounted to the top side 650 of the chassis-less circuitboard substrate 602 such that no two heat-producing, electricalcomponents shadow each other. In the illustrative embodiment, theprocessors 820 and communication circuit 830 are mounted incorresponding locations on the top side 650 of the chassis-less circuitboard substrate 602 such that no two of those physical resources arelinearly in-line with others along the direction of the airflow path608. It should be appreciated that, although the optical data connector834 is in-line with the communication circuit 830, the optical dataconnector 834 produces no or nominal heat during operation.

The memory devices 720 of the compute sled 800 are mounted to the bottomside 750 of the of the chassis-less circuit board substrate 602 asdiscussed above in regard to the sled 400. Although mounted to thebottom side 750, the memory devices 720 are communicatively coupled tothe processors 820 located on the top side 650 via the I/O subsystem622. Because the chassis-less circuit board substrate 602 is embodied asa double-sided circuit board, the memory devices 720 and the processors820 may be communicatively coupled by one or more vias, connectors, orother mechanisms extending through the chassis-less circuit boardsubstrate 602. Of course, each processor 820 may be communicativelycoupled to a different set of one or more memory devices 720 in someembodiments. Alternatively, in other embodiments, each processor 820 maybe communicatively coupled to each memory device 720. In someembodiments, the memory devices 720 may be mounted to one or more memorymezzanines on the bottom side of the chassis-less circuit boardsubstrate 602 and may interconnect with a corresponding processor 820through a ball-grid array.

Each of the processors 820 includes a heatsink 850 secured thereto. Dueto the mounting of the memory devices 720 to the bottom side 750 of thechassis-less circuit board substrate 602 (as well as the verticalspacing of the sleds 400 in the corresponding rack 240), the top side650 of the chassis-less circuit board substrate 602 includes additional“free” area or space that facilitates the use of heatsinks 850 having alarger size relative to traditional heatsinks used in typical servers.Additionally, due to the improved thermal cooling characteristics of thechassis-less circuit board substrate 602, none of the processorheatsinks 850 include cooling fans attached thereto. That is, each ofthe heatsinks 850 is embodied as a fan-less heatsinks.

Referring now to FIG. 10, in some embodiments, the sled 400 may beembodied as an accelerator sled 1000. The accelerator sled 1000 isoptimized, or otherwise configured, to perform specialized computetasks, such as machine learning, encryption, hashing, or othercomputational-intensive task. In some embodiments, for example, acompute sled 800 may offload tasks to the accelerator sled 1000 duringoperation. The accelerator sled 1000 includes various components similarto components of the sled 400 and/or compute sled 800, which have beenidentified in FIG. 10 using the same reference numbers. The descriptionof such components provided above in regard to FIGS. 6, 7, and 8 applyto the corresponding components of the accelerator sled 1000 and is notrepeated herein for clarity of the description of the accelerator sled1000.

In the illustrative accelerator sled 1000, the physical resources 620are embodied as accelerator circuits 1020. Although only two acceleratorcircuits 1020 are shown in FIG. 10, it should be appreciated that theaccelerator sled 1000 may include additional accelerator circuits 1020in other embodiments. For example, as shown in FIG. 11, the acceleratorsled 1000 may include four accelerator circuits 1020 in someembodiments. The accelerator circuits 1020 may be embodied as any typeof processor, co-processor, compute circuit, or other device capable ofperforming compute or processing operations. For example, theaccelerator circuits 1020 may be embodied as, for example, fieldprogrammable gate arrays (FPGA), application-specific integratedcircuits (ASICs), security co-processors, graphics processing units(GPUs), machine learning circuits, or other specialized processors,controllers, devices, and/or circuits.

In some embodiments, the accelerator sled 1000 may also include anaccelerator-to-accelerator interconnect 1042. Similar to theresource-to-resource interconnect 624 of the sled 600 discussed above,the accelerator-to-accelerator interconnect 1042 may be embodied as anytype of communication interconnect capable of facilitatingaccelerator-to-accelerator communications. In the illustrativeembodiment, the accelerator-to-accelerator interconnect 1042 is embodiedas a high-speed point-to-point interconnect (e.g., faster than the I/Osubsystem 622). For example, the accelerator-to-accelerator interconnect1042 may be embodied as a QuickPath Interconnect (QPI), an UltraPathInterconnect (UPI), or other high-speed point-to-point interconnectdedicated to processor-to-processor communications. In some embodiments,the accelerator circuits 1020 may be daisy-chained with a primaryaccelerator circuit 1020 connected to the NIC 832 and memory 720 throughthe I/O subsystem 622 and a secondary accelerator circuit 1020 connectedto the NIC 832 and memory 720 through a primary accelerator circuit1020.

Referring now to FIG. 11, an illustrative embodiment of the acceleratorsled 1000 is shown. As discussed above, the accelerator circuits 1020,communication circuit 830, and optical data connector 834 are mounted tothe top side 650 of the chassis-less circuit board substrate 602. Again,the individual accelerator circuits 1020 and communication circuit 830are mounted to the top side 650 of the chassis-less circuit boardsubstrate 602 such that no two heat-producing, electrical componentsshadow each other as discussed above. The memory devices 720 of theaccelerator sled 1000 are mounted to the bottom side 750 of the of thechassis-less circuit board substrate 602 as discussed above in regard tothe sled 600. Although mounted to the bottom side 750, the memorydevices 720 are communicatively coupled to the accelerator circuits 1020located on the top side 650 via the I/O subsystem 622 (e.g., throughvias). Further, each of the accelerator circuits 1020 may include aheatsink 1070 that is larger than a traditional heatsink used in aserver. As discussed above with reference to the heatsinks 870, theheatsinks 1070 may be larger than tradition heatsinks because of the“free” area provided by the memory devices 750 being located on thebottom side 750 of the chassis-less circuit board substrate 602 ratherthan on the top side 650.

Referring now to FIG. 12, in some embodiments, the sled 400 may beembodied as a storage sled 1200. The storage sled 1200 is optimized, orotherwise configured, to store data in a data storage 1250 local to thestorage sled 1200. For example, during operation, a compute sled 800 oran accelerator sled 1000 may store and retrieve data from the datastorage 1250 of the storage sled 1200. The storage sled 1200 includesvarious components similar to components of the sled 400 and/or thecompute sled 800, which have been identified in FIG. 12 using the samereference numbers. The description of such components provided above inregard to FIGS. 6, 7, and 8 apply to the corresponding components of thestorage sled 1200 and is not repeated herein for clarity of thedescription of the storage sled 1200.

In the illustrative storage sled 1200, the physical resources 620 areembodied as storage controllers 1220. Although only two storagecontrollers 1220 are shown in FIG. 12, it should be appreciated that thestorage sled 1200 may include additional storage controllers 1220 inother embodiments. The storage controllers 1220 may be embodied as anytype of processor, controller, or control circuit capable of controllingthe storage and retrieval of data into the data storage 1250 based onrequests received via the communication circuit 830. In the illustrativeembodiment, the storage controllers 1220 are embodied as relativelylow-power processors or controllers. For example, in some embodiments,the storage controllers 1220 may be configured to operate at a powerrating of about 75 watts.

In some embodiments, the storage sled 1200 may also include acontroller-to-controller interconnect 1242. Similar to theresource-to-resource interconnect 624 of the sled 400 discussed above,the controller-to-controller interconnect 1242 may be embodied as anytype of communication interconnect capable of facilitatingcontroller-to-controller communications. In the illustrative embodiment,the controller-to-controller interconnect 1242 is embodied as ahigh-speed point-to-point interconnect (e.g., faster than the I/Osubsystem 622). For example, the controller-to-controller interconnect1242 may be embodied as a QuickPath Interconnect (QPI), an UltraPathInterconnect (UPI), or other high-speed point-to-point interconnectdedicated to processor-to-processor communications.

Referring now to FIG. 13, an illustrative embodiment of the storage sled1200 is shown. In the illustrative embodiment, the data storage 1250 isembodied as, or otherwise includes, a storage cage 1252 configured tohouse one or more solid state drives (SSDs) 1254. To do so, the storagecage 1252 includes a number of mounting slots 1256, each of which isconfigured to receive a corresponding solid state drive 1254. Each ofthe mounting slots 1256 includes a number of drive guides 1258 thatcooperate to define an access opening 1260 of the corresponding mountingslot 1256. The storage cage 1252 is secured to the chassis-less circuitboard substrate 602 such that the access openings face away from (i.e.,toward the front of) the chassis-less circuit board substrate 602. Assuch, solid state drives 1254 are accessible while the storage sled 1200is mounted in a corresponding rack 204. For example, a solid state drive1254 may be swapped out of a rack 240 (e.g., via a robot) while thestorage sled 1200 remains mounted in the corresponding rack 240.

The storage cage 1252 illustratively includes sixteen mounting slots1256 and is capable of mounting and storing sixteen solid state drives1254. Of course, the storage cage 1252 may be configured to storeadditional or fewer solid state drives 1254 in other embodiments.Additionally, in the illustrative embodiment, the solid state driversare mounted vertically in the storage cage 1252, but may be mounted inthe storage cage 1252 in a different orientation in other embodiments.Each solid state drive 1254 may be embodied as any type of data storagedevice capable of storing long term data. To do so, the solid statedrives 1254 may include volatile and non-volatile memory devicesdiscussed above.

As shown in FIG. 13, the storage controllers 1220, the communicationcircuit 830, and the optical data connector 834 are illustrativelymounted to the top side 650 of the chassis-less circuit board substrate602. Again, as discussed above, any suitable attachment or mountingtechnology may be used to mount the electrical components of the storagesled 1200 to the chassis-less circuit board substrate 602 including, forexample, sockets (e.g., a processor socket), holders, brackets, solderedconnections, and/or other mounting or securing techniques.

As discussed above, the individual storage controllers 1220 and thecommunication circuit 830 are mounted to the top side 650 of thechassis-less circuit board substrate 602 such that no twoheat-producing, electrical components shadow each other. For example,the storage controllers 1220 and the communication circuit 830 aremounted in corresponding locations on the top side 650 of thechassis-less circuit board substrate 602 such that no two of thoseelectrical components are linearly in-line with other along thedirection of the airflow path 608.

The memory devices 720 of the storage sled 1200 are mounted to thebottom side 750 of the of the chassis-less circuit board substrate 602as discussed above in regard to the sled 400. Although mounted to thebottom side 750, the memory devices 720 are communicatively coupled tothe storage controllers 1220 located on the top side 650 via the I/Osubsystem 622. Again, because the chassis-less circuit board substrate602 is embodied as a double-sided circuit board, the memory devices 720and the storage controllers 1220 may be communicatively coupled by oneor more vias, connectors, or other mechanisms extending through thechassis-less circuit board substrate 602. Each of the storagecontrollers 1220 includes a heatsink 1270 secured thereto. As discussedabove, due to the improved thermal cooling characteristics of thechassis-less circuit board substrate 602 of the storage sled 1200, noneof the heatsinks 1270 include cooling fans attached thereto. That is,each of the heatsinks 1270 is embodied as a fan-less heatsink.

Referring now to FIG. 14, in some embodiments, the sled 400 may beembodied as a memory sled 1400. The storage sled 1400 is optimized, orotherwise configured, to provide other sleds 400 (e.g., compute sleds800, accelerator sleds 1000, etc.) with access to a pool of memory(e.g., in two or more sets 1430, 1432 of memory devices 720) local tothe memory sled 1200. For example, during operation, a compute sled 800or an accelerator sled 1000 may remotely write to and/or read from oneor more of the memory sets 1430, 1432 of the memory sled 1200 using alogical address space that maps to physical addresses in the memory sets1430, 1432. The memory sled 1400 includes various components similar tocomponents of the sled 400 and/or the compute sled 800, which have beenidentified in FIG. 14 using the same reference numbers. The descriptionof such components provided above in regard to FIGS. 6, 7, and 8 applyto the corresponding components of the memory sled 1400 and is notrepeated herein for clarity of the description of the memory sled 1400.

In the illustrative memory sled 1400, the physical resources 620 areembodied as memory controllers 1420. Although only two memorycontrollers 1420 are shown in FIG. 14, it should be appreciated that thememory sled 1400 may include additional memory controllers 1420 in otherembodiments. The memory controllers 1420 may be embodied as any type ofprocessor, controller, or control circuit capable of controlling thewriting and reading of data into the memory sets 1430, 1432 based onrequests received via the communication circuit 830. In the illustrativeembodiment, each storage controller 1220 is connected to a correspondingmemory set 1430, 1432 to write to and read from memory devices 720within the corresponding memory set 1430, 1432 and enforce anypermissions (e.g., read, write, etc.) associated with sled 400 that hassent a request to the memory sled 1400 to perform a memory accessoperation (e.g., read or write).

In some embodiments, the memory sled 1400 may also include acontroller-to-controller interconnect 1442. Similar to theresource-to-resource interconnect 624 of the sled 400 discussed above,the controller-to-controller interconnect 1442 may be embodied as anytype of communication interconnect capable of facilitatingcontroller-to-controller communications. In the illustrative embodiment,the controller-to-controller interconnect 1442 is embodied as ahigh-speed point-to-point interconnect (e.g., faster than the I/Osubsystem 622). For example, the controller-to-controller interconnect1442 may be embodied as a QuickPath Interconnect (QPI), an UltraPathInterconnect (UPI), or other high-speed point-to-point interconnectdedicated to processor-to-processor communications. As such, in someembodiments, a memory controller 1420 may access, through thecontroller-to-controller interconnect 1442, memory that is within thememory set 1432 associated with another memory controller 1420. In someembodiments, a scalable memory controller is made of multiple smallermemory controllers, referred to herein as “chiplets”, on a memory sled(e.g., the memory sled 1400). The chiplets may be interconnected (e.g.,using EMIB (Embedded Multi-Die Interconnect Bridge)). The combinedchiplet memory controller may scale up to a relatively large number ofmemory controllers and I/O ports, (e.g., up to 16 memory channels). Insome embodiments, the memory controllers 1420 may implement a memoryinterleave (e.g., one memory address is mapped to the memory set 1430,the next memory address is mapped to the memory set 1432, and the thirdaddress is mapped to the memory set 1430, etc.). The interleaving may bemanaged within the memory controllers 1420, or from CPU sockets (e.g.,of the compute sled 800) across network links to the memory sets 1430,1432, and may improve the latency associated with performing memoryaccess operations as compared to accessing contiguous memory addressesfrom the same memory device.

Further, in some embodiments, the memory sled 1400 may be connected toone or more other sleds 400 (e.g., in the same rack 240 or an adjacentrack 240) through a waveguide, using the waveguide connector 1480. Inthe illustrative embodiment, the waveguides are 64 millimeter waveguidesthat provide 16 Rx (i.e., receive) lanes and 16 Rt (i.e., transmit)lanes. Each lane, in the illustrative embodiment, is either 16 Ghz or 32Ghz. In other embodiments, the frequencies may be different. Using awaveguide may provide high throughput access to the memory pool (e.g.,the memory sets 1430, 1432) to another sled (e.g., a sled 400 in thesame rack 240 or an adjacent rack 240 as the memory sled 1400) withoutadding to the load on the optical data connector 834.

Referring now to FIG. 15, a system for executing one or more workloads(e.g., applications) may be implemented in accordance with the datacenter 100. In the illustrative embodiment, the system 1510 includes anorchestrator server 1520, which may be embodied as a managed nodecomprising a compute device (e.g., a compute sled 800) executingmanagement software (e.g., a cloud operating environment, such asOpenStack) that is communicatively coupled to multiple sleds 400including a large number of compute sleds 1530 (e.g., each similar tothe compute sled 800), memory sleds 1540 (e.g., each similar to thememory sled 1400), accelerator sleds 1550 (e.g., each similar to thememory sled 1000), and storage sleds 1560 (e.g., each similar to thestorage sled 1200). One or more of the sleds 1530, 1540, 1550, 1560 maybe grouped into a managed node 1570, such as by the orchestrator server1520, to collectively perform a workload (e.g., an application 1532executed in a virtual machine or in a container). The managed node 1570may be embodied as an assembly of physical resources 620, such asprocessors 820, memory resources 720, accelerator circuits 1020, or datastorage 1250, from the same or different sleds 400. Further, the managednode may be established, defined, or “spun up” by the orchestratorserver 1520 at the time a workload is to be assigned to the managed nodeor at any other time, and may exist regardless of whether any workloadsare presently assigned to the managed node. In the illustrativeembodiment, the orchestrator server 1520 may selectively allocate and/ordeallocate physical resources 620 from the sleds 400 and/or add orremove one or more sleds 400 from the managed node 1570 as a function ofquality of service (QoS) targets (e.g., performance targets associatedwith a throughput, latency, instructions per second, etc.) associatedwith a service level agreement for the workload (e.g., the application1532). In doing so, the orchestrator server 1520 may receive telemetrydata indicative of performance conditions (e.g., throughput, latency,instructions per second, etc.) in each sled 400 of the managed node 1570and compare the telemetry data to the quality of service targets todetermine whether the quality of service targets are being satisfied. Ifthe so, the orchestrator server 1520 may additionally determine whetherone or more physical resources may be deallocated from the managed node1570 while still satisfying the QoS targets, thereby freeing up thosephysical resources for use in another managed node (e.g., to execute adifferent workload). Alternatively, if the QoS targets are not presentlysatisfied, the orchestrator server 1520 may determine to dynamicallyallocate additional physical resources to assist in the execution of theworkload (e.g., the application 1532) while the workload is executing

Additionally, in some embodiments, the orchestrator server 1520 mayidentify trends in the resource utilization of the workload (e.g., theapplication 1532), such as by identifying phases of execution (e.g.,time periods in which different operations, each having differentresource utilizations characteristics, are performed) of the workload(e.g., the application 1532) and pre-emptively identifying availableresources in the data center 100 and allocating them to the managed node1570 (e.g., within a predefined time period of the associated phasebeginning). In some embodiments, the orchestrator server 1520 may modelperformance based on various latencies and a distribution scheme toplace workloads among compute sleds and other resources (e.g.,accelerator sleds, memory sleds, storage sleds) in the data center 100.For example, the orchestrator server 1520 may utilize a model thataccounts for the performance of resources on the sleds 400 (e.g., FPGAperformance, memory access latency, etc.) and the performance (e.g.,congestion, latency, bandwidth) of the path through the network to theresource (e.g., FPGA). As such, the orchestrator server 1520 maydetermine which resource(s) should be used with which workloads based onthe total latency associated with each potential resource available inthe data center 100 (e.g., the latency associated with the performanceof the resource itself in addition to the latency associated with thepath through the network between the compute sled executing the workloadand the sled 400 on which the resource is located).

In some embodiments, the orchestrator server 1520 may generate a map ofheat generation in the data center 100 using telemetry data (e.g.,temperatures, fan speeds, etc.) reported from the sleds 400 and allocateresources to managed nodes as a function of the map of heat generationand predicted heat generation associated with different workloads, tomaintain a target temperature and heat distribution in the data center100. Additionally or alternatively, in some embodiments, theorchestrator server 1520 may organize received telemetry data into ahierarchical model that is indicative of a relationship between themanaged nodes (e.g., a spatial relationship such as the physicallocations of the resources of the managed nodes within the data center100 and/or a functional relationship, such as groupings of the managednodes by the customers the managed nodes provide services for, the typesof functions typically performed by the managed nodes, managed nodesthat typically share or exchange workloads among each other, etc.).Based on differences in the physical locations and resources in themanaged nodes, a given workload may exhibit different resourceutilizations (e.g., cause a different internal temperature, use adifferent percentage of processor or memory capacity) across theresources of different managed nodes. The orchestrator server 1520 maydetermine the differences based on the telemetry data stored in thehierarchical model and factor the differences into a prediction offuture resource utilization of a workload if the workload is reassignedfrom one managed node to another managed node, to accurately balanceresource utilization in the data center 100.

To reduce the computational load on the orchestrator server 1520 and thedata transfer load on the network, in some embodiments, the orchestratorserver 1520 may send self-test information to the sleds 400 to enableeach sled 400 to locally (e.g., on the sled 400) determine whethertelemetry data generated by the sled 400 satisfies one or moreconditions (e.g., an available capacity that satisfies a predefinedthreshold, a temperature that satisfies a predefined threshold, etc.).Each sled 400 may then report back a simplified result (e.g., yes or no)to the orchestrator server 1520, which the orchestrator server 1520 mayutilize in determining the allocation of resources to managed nodes.

Referring now to FIG. 16, a system 1610 for managing errors in aremotely accessible memory pool may be implemented in accordance withthe data center 100 described above with reference to FIG. 1. In theillustrative embodiment, the system 1610 includes an orchestrator server1620 communicatively coupled to multiple sleds. The sleds include amemory sled 1640 and a set of compute sleds 1630, including computesleds 1632, 1634, 1636. The memory sled 1640 includes a memory poolcontroller 1660 connected to multiple memory devices 1672 that,together, form a memory pool 1670 (also referred to herein as the memory1670). The memory pool controller 1660 may be embodied as any device orcircuitry (e.g., microcontrollers, processors, ASICs, FPGAs) capable ofproviding access to the byte-addressable memory in the memory pool 1670to multiple sleds (e.g., the compute sleds 1630). In some embodiments,multiple memory sleds 1640 may form the memory pool 1670.

One or more of the sleds 1630, 1640 may be grouped into a managed node,such as by the orchestrator server 1620, to collectively perform one ormore workloads 1650, 1652, 1654, such as in virtual machines orcontainers, on behalf of a user of the client device 1614. A managednode may be embodied as an assembly of resources, such as computeresources, memory resources, storage resources, or other resources, fromthe same or different sleds or racks. Further, a managed node may beestablished, defined, or “spun up” by the orchestrator server 1620 atthe time a workload is to be assigned to the managed node or at anyother time, and may exist regardless of whether any workloads arepresently assigned to the managed node. The orchestrator server 1620 maysupport a cloud operating environment, such as OpenStack.

In the illustrative embodiment, in operation, the memory sled 1640allocates byte-addressable (e.g., addressable by one or more bytes, lessthan a block) memory from the memory pool 1670 for use by one or more ofthe compute sleds 1630 as the compute sleds 1630 execute correspondingworkloads 1650, 1652, 1654, detects whether one or more regions of thememory pool 1670 are faulty (e.g., producing corruption in data that iswritten to the region of the byte-addressable memory), and if so, sendsa notification to a compute device, such as the orchestrator server 1620and/or the corresponding compute sled 1630 (e.g., the compute sledhaving an address space that is mapped to the faulty region), to enablethe compute device to act upon the notification (e.g., by requesting thememory sled to move the data in the faulty memory region to a newlyallocated memory region, such as after an error correction algorithm hasbeen executed on the data to correct any corruption in the data). Assuch, the system 1610 provides proactive management of faulty memory ina shared, remotely-accessible memory pool 1670 before the data is lost.While the memory sled 1640 is described in herein as performing theabove functions with respect to compute sleds 1630, it should beappreciated that the memory sled 1640 may perform similar functions forother types of sleds (e.g., accelerator sleds) that are to access memoryin the memory pool 1670.

Referring now to FIG. 17, the memory sled 1640 may be embodied as anytype of compute device capable of performing the functions describedherein, including performing data access operations on the memory in thememory pool 1670 in response to memory access requests from the computesleds 1630, writing test data to a byte-addressable memory region in thememory pool 1670, reading data from the memory region to which the testdata was written, comparing the read data to the test data to determinewhether a threshold number of errors are present in the read data, andsending, in response to a determination that the threshold number oferrors are present in the read data, a notification to the remotecompute sled 1630 that the memory region is faulty.

As shown in FIG. 17, the illustrative memory sled 1640 includes acompute engine 1702, an input/output (I/O) subsystem 1704, andcommunication circuitry 1706. Of course, in other embodiments, thememory sled 1640 may include other or additional components, such asthose commonly found in a computer (e.g., display, peripheral devices,etc.). Additionally, in some embodiments, one or more of theillustrative components may be incorporated in, or otherwise form aportion of, another component. The compute engine 1702 may be embodiedas any type of device or collection of devices capable of performingvarious compute functions described below. In some embodiments, thecompute engine 1702 may be embodied as a single device such as anintegrated circuit, an embedded system, a field-programmable gate array(FPGA), a system-on-a-chip (SOC), or other integrated system or device.In the illustrative embodiment, the compute engine 1702 includes or isembodied as the memory pool controller 1660 and the memory pool 1670(also referred to herein as memory). The memory pool controller 1660 maybe embodied as any type of device or circuitry capable of performing thefunctions described herein. For example, the memory pool controller 1660may be embodied as a single or multi-core processor(s), amicrocontroller, or other processor or processing/controlling circuit.In some embodiments, the memory pool controller 1660 may be embodied as,include, or be coupled to an FPGA, an application specific integratedcircuit (ASIC), reconfigurable hardware or hardware circuitry, or otherspecialized hardware to facilitate performance of the functionsdescribed herein. In the illustrative embodiment, the memory poolcontroller includes a memory condition logic unit 1720, which may beembodied as any device or circuitry (e.g., processor(s), ASICs, FPGAs,etc.) capable of determining the condition of memory regions in thememory pool 1670, such as by writing data to a memory region, readingthe data back, and comparing the read data to the written data to detecterrors attributable to the condition of the memory region (e.g., thecondition of the underlying memory device 1672). In other embodiments,the memory 1670 may report any detected errors using a similar process.

The memory 1670 may be embodied as any type of volatile (e.g., dynamicrandom access memory (DRAM), etc.) or non-volatile memory or datastorage capable of performing the functions described herein. Volatilememory may be a storage medium that requires power to maintain the stateof data stored by the medium. Non-limiting examples of volatile memorymay include various types of random access memory (RAM), such as dynamicrandom access memory (DRAM) or static random access memory (SRAM). Oneparticular type of DRAM that may be used in a memory module issynchronous dynamic random access memory (SDRAM). In particularembodiments, DRAM of a memory component may comply with a standardpromulgated by JEDEC, such as JESD79F for DDR SDRAM, JESD79-2F for DDR2SDRAM, JESD79-3F for DDR3 SDRAM, JESD79-4A for DDR4 SDRAM, JESD209 forLow Power DDR (LPDDR), JESD209-2 for LPDDR2, JESD209-3 for LPDDR3, andJESD209-4 for LPDDR4 (these standards are available at www.jedec.org).Such standards (and similar standards) may be referred to as DDR-basedstandards and communication interfaces of the storage devices thatimplement such standards may be referred to as DDR-based interfaces.

In one embodiment, the memory device is a block addressable memorydevice, such as those based on NAND or NOR technologies. A memory devicemay also include future generation nonvolatile devices, such as a threedimensional crosspoint memory device (e.g., Intel 3D XPoint™ memory), orother byte-addressable write-in-place nonvolatile memory devices. In oneembodiment, the memory device may be or may include memory devices thatuse chalcogenide glass, multi-threshold level NAND flash memory, NORflash memory, single or multi-level Phase Change Memory (PCM), aresistive memory, nanowire memory, ferroelectric transistor randomaccess memory (FeTRAM), anti-ferroelectric memory, magnetoresistiverandom access memory (MRAM) memory that incorporates memristortechnology, resistive memory including the metal oxide base, the oxygenvacancy base and the conductive bridge Random Access Memory (CB-RAM), orspin transfer torque (STT)-MRAM, a spintronic magnetic junction memorybased device, a magnetic tunneling junction (MTJ) based device, a DW(Domain Wall) and SOT (Spin Orbit Transfer) based device, a thyristorbased memory device, or a combination of any of the above, or othermemory. The memory device may refer to the die itself and/or to apackaged memory product.

In some embodiments, 3D crosspoint memory (e.g., Intel 3D XPoint™memory) may comprise a transistor-less stackable cross pointarchitecture in which memory cells sit at the intersection of word linesand bit lines and are individually addressable and in which bit storageis based on a change in bulk resistance. In some embodiments, all or aportion of the memory 1670 may be integrated into the memory poolcontroller 1660. In operation, the memory 1670 may store varioussoftware and data used during operation such as memory map data, memorycondition data, workload data, applications, programs, and libraries.

The compute engine 1702 is communicatively coupled to other componentsof the memory sled 1640 via the I/O subsystem 1704, which may beembodied as circuitry and/or components to facilitate input/outputoperations with the compute engine 1702 (e.g., with the memory poolcontroller 1660 and/or the memory 1670) and other components of thememory sled 1640. For example, the I/O subsystem 1704 may be embodiedas, or otherwise include, memory controller hubs, input/output controlhubs, integrated sensor hubs, firmware devices, communication links(e.g., point-to-point links, bus links, wires, cables, light guides,printed circuit board traces, etc.), and/or other components andsubsystems to facilitate the input/output operations. In someembodiments, the I/O subsystem 1704 may form a portion of asystem-on-a-chip (SoC) and be incorporated, along with one or more ofthe memory pool controller 1660, the memory 1670, and other componentsof the memory sled 1640, into the compute engine 1702.

The communication circuitry 1706 may be embodied as any communicationcircuit, device, or collection thereof, capable of enablingcommunications over the network 1612 between the memory sled 1640 andanother compute device (e.g., the compute sleds 1630, the orchestratorserver 1620). The communication circuitry 1708 may be configured to useany one or more communication technology (e.g., wired or wirelesscommunications) and associated protocols (e.g., Ethernet, Bluetooth®,Wi-Fi®, WiMAX, etc.) to effect such communication.

The communication circuitry 1706 may include a network interfacecontroller (NIC) 1708, which may also be referred to as a host fabricinterface (HFI). The NIC 1708 may be embodied as one or moreadd-in-boards, daughter cards, network interface cards, controllerchips, chipsets, or other devices that may be used by the memory sled1640 to connect with another compute device (e.g., the compute sleds1630, the orchestrator server 1620, etc.). In some embodiments, the NIC1708 may be embodied as part of a system-on-a-chip (SoC) that includesone or more processors, or included on a multichip package that alsocontains one or more processors. In some embodiments, the NIC 1708 mayinclude a local processor (not shown) and/or a local memory (not shown)that are both local to the NIC 1708. In such embodiments, the localprocessor of the NIC 1708 may be capable of performing one or more ofthe functions of the compute engine 1702 described herein. Additionallyor alternatively, in such embodiments, the local memory of the NIC 1708may be integrated into one or more components of the memory sled 1640 atthe board level, socket level, chip level, and/or other levels.

The memory sled 1640 may also include one or more data storage devices1710, which may be embodied as any type of devices configured forshort-term or long-term storage of data such as, for example, memorydevices and circuits, memory cards, hard disk drives, solid-statedrives, or other data storage devices. Each data storage device 1710 mayinclude a system partition that stores data and firmware code for thedata storage device 1710. Each data storage device 1710 may also includeone or more operating system partitions that store data files andexecutables for operating systems.

The orchestrator server 1620, the compute sleds 1630, and the clientdevice 1614 may have components similar to those described in FIG. 17.The description of those components of the memory sled 1640 is equallyapplicable to the description of components of those devices and is notrepeated herein for clarity of the description. Further, it should beappreciated that any of the memory sled 1640, the compute sled 1630, theorchestrator server 1620, or the client device 1614 may include othercomponents, sub-components, and devices commonly found in a computingdevice, which are not discussed above in reference to the memory sled1640 and not discussed herein for clarity of the description.

As described above, the orchestrator server 1620, the sleds 1630, 1640,and the client device 1614 are illustratively in communication via thenetwork 1612, which may be embodied as any type of wired or wirelesscommunication network, including global networks (e.g., the Internet),local area networks (LANs) or wide area networks (WANs), cellularnetworks (e.g., Global System for Mobile Communications (GSM), 3G, LongTerm Evolution (LTE), Worldwide Interoperability for Microwave Access(WiMAX), etc.), digital subscriber line (DSL) networks, cable networks(e.g., coaxial networks, fiber networks, etc.), or any combinationthereof.

Referring now to FIG. 18, the memory sled 1640 may establish anenvironment 1800 during operation. The illustrative environment 1800includes a network communicator 1820 and a memory manager 1830. Each ofthe components of the environment 1800 may be embodied as hardware,firmware, software, or a combination thereof. As such, in someembodiments, one or more of the components of the environment 1800 maybe embodied as circuitry or a collection of electrical devices (e.g.,network communicator circuitry 1820, memory manager circuitry 1830,etc.). It should be appreciated that, in such embodiments, one or moreof the network communicator circuitry 1820 or memory manager circuitry1830 may form a portion of one or more of the compute engine 1702, thememory pool controller 1660, the memory condition logic unit 1720, thememory 1670, the communication circuitry 1708, the I/O subsystem 1704and/or other components of the memory sled 1640. In the illustrativeembodiment, the environment 1800 includes memory map data 1802, whichmay be embodied as any data indicative of physical addresses of thememory 1670, corresponding logical addresses (e.g., addresses used bythe memory pool controller 1660 and the compute sleds 1630 that aremapped to all or a subset of the physical addresses), and permissionsassociated with one or more compute sleds 1630 indicative of whether alogical address is accessible (e.g., within a memory space of logicaladdresses) available to the compute sled 1630 for read and/or writeaccess. Additionally, in the illustrative embodiment, the environment1800 includes memory condition data 1804 which may be embodied as anydata indicative of regions of the memory (e.g., physical addresses) thatthe memory sled 1640 has determined to be faulty. The illustrativeenvironment 1800 also includes remotely accessible data 1806 which maybe embodied as any data present in the memory 1670 that is available to(e.g., within an address space) provided to one or more correspondingcompute sleds 1630.

In the illustrative environment 1800, the network communicator 1820,which may be embodied as hardware, firmware, software, virtualizedhardware, emulated architecture, and/or a combination thereof asdiscussed above, is configured to facilitate inbound and outboundnetwork communications (e.g., network traffic, network packets, networkflows, etc.) to and from the accelerator sled 1640, respectively. To doso, the network communicator 1820 is configured to receive and processdata packets from one system or computing device (e.g., a compute sled1630, the orchestrator server 1620, etc.) and to prepare and send datapackets to a computing device or system (e.g., a compute sled 1630, theorchestrator server 1620, etc.). Accordingly, in some embodiments, atleast a portion of the functionality of the network communicator 1820may be performed by the communication circuitry 1706, and, in theillustrative embodiment, by the NIC 1708.

The memory manager 1830, which may be embodied as hardware, firmware,software, virtualized hardware, emulated architecture, and/or acombination thereof, is configured to allocate byte-addressable memoryfrom the memory pool 1670 for use by one or more of the compute sleds1630, perform memory access operations (e.g., read and/or write) to thememory pool 1670 in response to memory access requests from the computesleds 1630, continually test the condition of the byte-addressablememory in the memory pool 1670 to determine whether any region of thememory is faulty, and if so, to send a report to remote compute device(e.g., a compute sled 1630 having an address space mapped to the faultymemory region and/or to the orchestrator server 1620) indicating thatthe memory region has been determined to be faulty. To do so, in theillustrative embodiment, the memory manager 1830 includes a memorymapper 1832, a memory condition manager 1834, a data writer 1836, and adata reader 1838. The memory mapper 1832, in the illustrativeembodiment, is configured to receive an allocation request from a remotecompute device (e.g., the orchestrator server 1620) to allocate one ormore regions of pooled byte-addressable memory (e.g., the memory 1670)to one or more compute sleds 1630, produce address space data for eachcompute sled 1630 indicative of the pooled byte-addressable memoryaccessible to the compute sled 1630, provide the address space data tothe compute sled(s) 1630, and convert addresses defined in any memoryaccess requests from the compute sled(s) 1630 to corresponding physicaladdresses in the memory pool 1670.

The memory condition manager 1834, in the illustrative embodiment, isconfigured to continually write data to regions of the byte-addressablememory 1670, read the data back, and determine whether differencesbetween the read data and the written data (e.g., errors) are indicativeof a memory region that should be designated as faulty (e.g., unusable).In doing so, the memory condition manager 1834 may determine whether thenumber of errors satisfies a threshold number of errors, such as anumber of errors that is equal to or within a predefined range of theerror correction capacity (e.g., the largest number of errors that canbe corrected) of a particular error correction algorithm (e.g., aReed-Solomon error correction algorithm, a low-density parity checkalgorithm, etc.) that may be executed by the memory sled 1640 in readingthe data and/or by the compute sleds 1630 upon receiving read data fromthe memory pool 1670. The memory condition manager 1834 may also send,through the network communicator 1820, a notification to a remotecompute device (e.g., a compute sled 1630 and/or the orchestrator server1620) that the memory region has been determined to be faulty. The datawriter 1836, in the illustrative embodiment, is configured to write datato the memory 1670 in response to a request (e.g., from the memorycondition manager 1834, from a compute sled 1630, etc.). Similarly, thedata reader 1838, in the illustrative embodiment, is configured to readdata from the memory 1670 in response to a request (e.g., from thememory condition manager 1834, from a compute sled 1630, etc.).

It should be appreciated that each of the memory mapper 1832, the memorycondition manager 1834, the data writer 1836, and the data reader 1838may be separately embodied as hardware, firmware, software, virtualizedhardware, emulated architecture, and/or a combination thereof. Forexample, the memory mapper 1832 may be embodied as a hardware component,while the memory condition manager 1834, the data writer 1836, and thedata reader 1838 are embodied as virtualized hardware components or assome other combination of hardware, firmware, software, virtualizedhardware, emulated architecture, and/or a combination thereof.

Referring now to FIG. 19, the memory sled 1640, in operation, mayexecute a method 1900 for managing errors in a memory pool (e.g., thememory 1670). The method 1900 begins with block 1902, in which thememory sled 1640 may receive an allocation request from a remote computedevice to allocate one or more regions of pooled byte-addressable memory(e.g., the memory 1670) to one or more compute sleds 1630. In theillustrative embodiment, the memory sled 1640 receives the request fromthe orchestrator server 1620, as indicated in block 1904. Alternatively,the memory sled 1640 may receive the allocation request from a computesled 1630, as indicated in block 1906. In some embodiments, theallocation request may be to allocate a new memory region after apreviously allocated memory region was determined to be faulty, asdescribed in more detail herein. Afterwards, in block 1908, the memorysled 1640 determines the subsequent course of action as a function ofwhether an allocation request was received. If an allocation request wasnot received, the method 1900 loops back to block 1902, in which thememory sled 1640 awaits an allocation request. Otherwise, the method1900 advances to block 1910 in which the memory sled 1640 allocatesbyte-addressable memory from the memory pool 1670 to the correspondingcompute sled(s) 1630. In doing so, the memory sled 1640, in theillustrative embodiment, produces address space data for each computesled 1630, as indicated in block 1912. The address space data isindicative of addresses of the pooled byte-addressable memory that areaccessible to the corresponding compute sled 1630. As indicated in block1914, in producing the address space data, the memory sled 1640 mayproduce a contiguous address space from non-contiguous memory addressesin the memory pool 1670 (e.g., map contiguous logical addresses tonon-contiguous physical addresses). Further, in the illustrativeembodiment, the memory sled 1640 sends the address space data to thecorresponding compute sled(s) 1630, as indicated in block 1916.

In block 1918, the memory sled 1640 performs memory access operationsfor the compute sled(s) 1630 (e.g., the compute sleds 1630 to which thememory sled 1640 sent address space data). In doing so, and as indicatedin block 1920, the memory sled 1640 may read data from the memory pool1670 in response to a memory access request from a compute sled 1630. Insome instances, as indicated in block 1922, the memory sled 1640 mayread data from a memory region that has been reported to be faulty, aspart of a data relocation process. As indicated in block 1924, thememory sled 1640 may write data in response to a memory access requestfrom a compute sled 1630. In doing so, and as indicated in block 1926,the memory sled 1640 may write a version of data that was read from afaulty memory region (e.g., in block 1922) and on which an errorcorrection algorithm was executed to corrected any errors in the readdata. The error correction algorithm may have been executed by therequesting compute sled 1630 (e.g., after receiving the read data fromblock 1922) or may be executed by the memory sled 1640. In writing thedata in block 1926, the memory sled 1640 writes the data to a differentmemory region than where it was read from (e.g., a newly allocatedmemory region that was allocated in block 1910) to relocate the data tonon-faulty memory.

Referring now to FIG. 20, the memory sled 1640, in block 1928, testsmemory regions of the byte-addressable memory in the memory pool 1670for faults. In doing so, and as indicated in block 1930, the memory sled1640 may test all of the memory in the memory pool 1670 regardless ofwhether the memory is presently allocated. In other embodiments, thememory sled 1640 may test only the memory that is presently allocated toa compute sled 1630. In block 1932, in testing the memory, the memorysled 1640 may write test data to a memory region. Further, in block1934, the memory sled 1640 may read data from a memory region andcompare it to previously-written data (e.g., the test data from block1932 or data that was requested to be written in block 1924) to detecterrors (e.g., a number of bits that do not match between the writtendata and the read data). In doing so, the memory sled 1640 may determinewhether the number of errors in the read data satisfies a predefinedthreshold, as indicated in block 1936. For example, the memory sled 1640may determine whether the number of errors in the read data is within apredefined range (e.g., within one or two errors) of a number of errorsthat a particular error correction algorithm used by the memory sled1640 or by the compute sleds 1630 is capable of correcting, as indicatedin block 1938. In doing so, the memory sled 1640 may determine whetherthe number of detected errors is within a predefined range of thecapability of a Reed-Solomon error correction algorithm, as indicated inblock 1940. In another example, the memory sled 1640 may determinewhether the number of detected errors is within a predefined range ofthe capability of a low-density parity check error correction algorithm,as indicated in block 1942.

In block 1944, the memory sled 1644 determines whether a faulty memoryregion was detected (e.g., whether the number of detected errorssatisfies the predefined threshold). If not, the method 1900 loops backto block 1918 of FIG. 19, in which the memory sled 1640 continues toperform memory access operations on behalf of the compute sleds 1630.Otherwise (e.g., if a faulty memory region was detected), the method1900 advances to block 1946 in which the memory sled 1640 reports thefaulty region (e.g., an address range of the faulty memory region) to aremote compute device. In doing so, the memory sled 1640 may compare thefaulty memory region to the address space(s) (e.g., the address spacedata) of the corresponding compute sled(s) 1630, as indicated in block1948. Further, the memory sled 1640 may send a notification of thefaulty memory region to the compute sled(s) 1630 having an address spaceto which the faulty memory region is mapped (e.g., determined from thecomparison performed in block 1948), as indicated in block 1950.Additionally or alternatively, the memory sled 1640 may send anotification of the faulty memory region to the orchestrator server1620, as indicated in block 1952. Either of the remote compute devices1630, 1620 may, in response, determine that replacement memory should beallocated from the memory pool 1670 to compensate for the faulty memoryregion, and send a corresponding allocation request to the memory sled1640. As such, in the illustrative embodiment, the method 1900 loopsback to block 1902 of FIG. 19 to await an allocation request. It shouldbe understood that while the blocks of the method 1900 are shown in aparticular order in FIGS. 19 and 20, the blocks may be performed in adifferent order or concurrently (e.g., the memory sled 1640 may performmemory access operations on behalf of the compute sleds 1630 whileconcurrently testing the memory in the memory pool 1670 for faults).

EXAMPLES

Illustrative examples of the technologies disclosed herein are providedbelow. An embodiment of the technologies may include any one or more,and any combination of, the examples described below.

Example 1 includes communication circuitry and a memory sled comprisinga memory pool controller couplable to a memory pool having one or morebyte-addressable memory devices, wherein the memory pool controller isto (i) write test data to a byte-addressable memory region in the memorypool, wherein the memory region is to be accessed by a remote computesled through a network; (ii) read data from the memory region to whichthe test data was written; (iii) compare the read data to the test datato determine whether a threshold number of errors are present in theread data; and (iv) send, in response to a determination that thethreshold number of errors are present in the read data, a notificationthrough the network that the memory region is faulty.

Example 2 includes the subject matter of Example 1, and wherein thememory pool controller is further to receive, from an orchestratorserver, an allocation request to allocate byte-addressable memory fromthe memory pool to the remote compute sled; and send, in response to adetermination that the threshold number of errors are present in theread data, a second notification to the orchestrator server that thememory region is faulty.

Example 3 includes the subject matter of any of Examples 1 and 2, andwherein the memory pool controller is further to produce address spacedata for the remote compute sled, wherein the address space data isindicative of addresses of the byte-addressable memory allocated to theremote compute sled; and send, to the remote compute sled, the addressspace data.

Example 4 includes the subject matter of any of Examples 1-3, andwherein to produce the address space data comprises to produce acontiguous address space from non-contiguous memory addresses in thememory pool.

Example 5 includes the subject matter of any of Examples 1-4, andwherein the memory pool controller is further to allocate, after thenotification that the memory region is faulty has been sent, a secondmemory region of the memory pool for use by the remote compute sled; andwrite, to the second memory region of the memory pool, anerror-corrected version of the data from the faulty memory region.

Example 6 includes the subject matter of any of Examples 1-5, andwherein the memory pool controller is further to test all of the memoryin the memory pool for faults.

Example 7 includes the subject matter of any of Examples 1-6, andwherein to determine whether a threshold number of errors are present inthe read data comprises to determine whether the number of presenterrors is within a predefined range of the number of errors that arecorrectable by an error correction algorithm

Example 8 includes the subject matter of any of Examples 1-7, andwherein to determine whether more than a number of errors that arecorrectable by an error correction algorithm are present in the readdata comprises to determine whether the number of present errors iswithin a predefined range of the number of errors that are correctableby a Reed-Solomon error correction algorithm

Example 9 includes the subject matter of any of Examples 1-8, andwherein to determine whether more than a number of errors that arecorrectable by an error correction algorithm are present in the readdata comprises to determine whether the number of present errors iswithin a predefined range of the number of errors that are correctableby a low-density parity check error correction algorithm.

Example 10 includes the subject matter of any of Examples 1-9, andwherein the remote compute sled is one of a plurality of remote computesleds to which byte-addressable memory from the memory pool has beenallocated and the memory pool controller is further to compare anaddress of the faulty memory region to an address space mapped to eachremote compute sled, and wherein to send the notification to the remotecompute sled comprises to send the notification to a remote compute sledthat has a memory address space to which the faulty memory region ismapped.

Example 11 includes the subject matter of any of Examples 1-10, andwherein the memory pool controller is further to perform, on the memorypool, a memory access operation for the remote compute sled.

Example 12 includes the subject matter of any of Examples 1-11, andwherein to perform a memory access operation comprises to write, inresponse to a memory access request from the remote compute sled, datato the memory pool.

Example 13 includes the subject matter of any of Examples 1-12, andwherein to perform a memory access operation comprises to read, inresponse to a memory access request from the remote compute sled, datafrom the memory pool.

Example 14 includes a method comprising writing, by a memory sled, testdata to a byte-addressable memory region of a memory pool, wherein thememory region is to be accessed by a remote compute sled through anetwork; reading, by the memory sled, data from the memory region towhich the test data was written; comparing, by the memory sled, the readdata to the test data to determine whether a threshold number of errorsare present in the read data; and sending, by the memory sled and inresponse to a determination that the threshold number of errors arepresent in the read data, a notification through the network that thememory region is faulty.

Example 15 includes the subject matter of Example 14, and furtherincluding receiving, from an orchestrator server and by the memory sled,an allocation request to allocate byte-addressable memory from thememory pool to the remote compute sled; and sending, by the memory sledand in response to a determination that the threshold number of errorsare present in the read data, a second notification to the orchestratorserver that the memory region is faulty.

Example 16 includes the subject matter of any of Examples 14 and 15, andfurther including producing, by the memory sled, address space data forthe remote compute sled, wherein the address space data is indicative ofaddresses of the byte-addressable memory allocated to the remote computesled; and sending, by the memory sled, the address space data to theremote compute sled.

Example 17 includes the subject matter of any of Examples 14-16, andwherein producing the address space data comprises producing acontiguous address space from non-contiguous memory addresses in thememory pool.

Example 18 includes the subject matter of any of Examples 14-17, andfurther including allocating, by the memory sled and after thenotification that the memory region is faulty has been sent, a secondmemory region of the memory pool for use by the remote compute sled; andwriting, by the memory sled and to the second memory region of thememory pool, an error-corrected version of the data from the faultymemory region.

Example 19 includes the subject matter of any of Examples 14-18, andfurther including testing, by the memory sled, all of the memory in thememory pool for faults.

Example 20 includes the subject matter of any of Examples 14-19, andwherein determining whether a threshold number of errors are present inthe read data comprises determining whether the number of present errorsis within a predefined range of the number of errors that arecorrectable by an error correction algorithm

Example 21 includes the subject matter of any of Examples 14-20, andwherein determining whether more than a number of errors that arecorrectable by an error correction algorithm are present in the readdata comprises determining whether the number of present errors iswithin a predefined range of the number of errors that are correctableby a Reed-Solomon error correction algorithm

Example 22 includes the subject matter of any of Examples 14-21, andwherein determining whether more than a number of errors that arecorrectable by an error correction algorithm are present in the readdata comprises determining whether the number of present errors iswithin a predefined range of the number of errors that are correctableby a low-density parity check error correction algorithm.

Example 23 includes the subject matter of any of Examples 14-22, andwherein the remote compute sled is one of a plurality of remote computesleds to which byte-addressable memory from the memory pool has beenallocated, the method further comprising comparing, by the memory sled,an address of the faulty memory region to an address space mapped toeach remote compute sled, and wherein sending the notification to theremote compute sled comprises sending the notification to a remotecompute sled that has a memory address space to which the faulty memoryregion is mapped.

Example 24 includes the subject matter of any of Examples 14-23, andfurther including performing, by the memory sled and on the memory pool,a memory access operation for the remote compute sled.

Example 25 includes the subject matter of any of Examples 14-24, andwherein performing a memory access operation comprises writing, inresponse to a memory access request from the remote compute sled, datato the memory pool.

Example 26 includes the subject matter of any of Examples 14-25, andwherein performing a memory access operation comprises reading, inresponse to a memory access request from the remote compute sled, datafrom the memory pool.

Example 27 includes a memory sled comprising means for performing themethod of any of Examples 14-26.

Example 28 includes one or more machine-readable storage mediacomprising a plurality of instructions stored thereon that, in responseto being executed, cause an memory sled to perform the method of any ofExamples 14-26.

Example 29 includes a memory sled comprising a compute engine to performthe method of any of Examples 14-26.

Example 30 includes a memory sled comprising means for writing test datato a byte-addressable memory region in a memory pool, wherein the memoryregion is to be accessed by a remote compute sled through a network;means for reading data from the memory region to which the test data waswritten; means for comparing the read data to the test data to determinewhether a threshold number of errors are present in the read data; andmeans for sending, in response to a determination that the thresholdnumber of errors are present in the read data, a notification throughthe network that the memory region is faulty.

Example 31 includes the subject matter of Example 30, and furtherincluding means for receiving, by the memory sled, an allocation requestto allocate byte-addressable memory from the memory pool to the remotecompute sled; and means for sending, in response to a determination thatthe threshold number of errors are present in the read data, a secondnotification to the orchestrator server that the memory region isfaulty.

Example 32 includes the subject matter of any of Examples 30 and 31, andfurther including means for producing address space data for the remotecompute sled, wherein the address space data is indicative of addressesof the byte-addressable memory allocated to the remote compute sled; andmeans for sending the address space data to the remote compute sled.

Example 33 includes the subject matter of any of Examples 30-32, andwherein the means for producing the address space data comprises meansfor producing a contiguous address space from non-contiguous memoryaddresses in the memory pool.

Example 34 includes the subject matter of any of Examples 30-33, andfurther including means for allocating, after the notification that thememory region is faulty has been sent, a second memory region of thememory pool for use by the remote compute sled; and means for writing,to the second memory region of the memory pool, an error-correctedversion of the data from the faulty memory region.

Example 35 includes the subject matter of any of Examples 30-34, andfurther including means for testing all of the memory in the memory poolfor faults.

Example 36 includes the subject matter of any of Examples 30-35, andwherein the means for determining whether a threshold number of errorsare present in the read data comprises means for determining whether thenumber of present errors is within a predefined range of the number oferrors that are correctable by an error correction algorithm.

Example 37 includes the subject matter of any of Examples 30-36, andwherein the means for determining whether more than a number of errorsthat are correctable by an error correction algorithm are present in theread data comprises means for determining whether the number of presenterrors is within a predefined range of the number of errors that arecorrectable by a Reed-Solomon error correction algorithm.

Example 38 includes the subject matter of any of Examples 30-37, andwherein the means for determining whether more than a number of errorsthat are correctable by an error correction algorithm are present in theread data comprises means for determining whether the number of presenterrors is within a predefined range of the number of errors that arecorrectable by a low-density parity check error correction algorithm.

Example 39 includes the subject matter of any of Examples 30-38, andwherein the remote compute sled is one of a plurality of remote computesleds to which byte-addressable memory from the memory pool has beenallocated, the memory sled further comprising means for comparing anaddress of the faulty memory region to an address space mapped to eachremote compute sled, and wherein the means for sending the notificationto the remote compute sled comprises means for sending the notificationto a remote compute sled that has a memory address space to which thefaulty memory region is mapped.

Example 40 includes the subject matter of any of Examples 30-39, andfurther including means for performing, on the memory pool, a memoryaccess operation for the remote compute sled.

Example 41 includes the subject matter of any of Examples 30-40, andwherein the means for performing a memory access operation comprisesmeans for writing, in response to a memory access request from theremote compute sled, data to the memory pool.

Example 42 includes the subject matter of any of Examples 30-41, andwherein the means for performing a memory access operation comprisesmeans for reading, in response to a memory access request from theremote compute sled, data from the memory pool.

1. A memory sled comprising: communication circuitry; a memory poolcontroller couplable to a memory pool having one or morebyte-addressable memory devices, wherein the memory pool controller isto: (i) write test data to a byte-addressable memory region in thememory pool, wherein the memory region is to be accessed by a remotecompute sled through a network; (ii) read data from the memory region towhich the test data was written; (iii) compare the read data to the testdata to determine whether a threshold number of errors are present inthe read data; and (iv) send, in response to a determination that thethreshold number of errors are present in the read data, a notificationthrough the network that the memory region is faulty.
 2. The memory sledof claim 1, wherein the memory pool controller is further to: receive,from an orchestrator server, an allocation request to allocatebyte-addressable memory from the memory pool to the remote compute sled;and send, in response to a determination that the threshold number oferrors are present in the read data, a second notification to theorchestrator server that the memory region is faulty.
 3. The memory sledof claim 1, wherein the memory pool controller is further to: produceaddress space data for the remote compute sled, wherein the addressspace data is indicative of addresses of the byte-addressable memoryallocated to the remote compute sled; and send, to the remote computesled, the address space data.
 4. The memory sled of claim 3, wherein toproduce the address space data comprises to produce a contiguous addressspace from non-contiguous memory addresses in the memory pool.
 5. Thememory sled of claim 1, wherein the memory pool controller is furtherto: allocate, after the notification that the memory region is faultyhas been sent, a second memory region of the memory pool for use by theremote compute sled; and write, to the second memory region of thememory pool, an error-corrected version of the data from the faultymemory region.
 6. The memory sled of claim 1, wherein the memory poolcontroller is further to test all of the memory in the memory pool forfaults.
 7. The memory sled of claim 1, wherein to determine whether athreshold number of errors are present in the read data comprises todetermine whether the number of present errors is within a predefinedrange of the number of errors that are correctable by an errorcorrection algorithm
 8. The memory sled of claim 7, wherein to determinewhether more than a number of errors that are correctable by an errorcorrection algorithm are present in the read data comprises to determinewhether the number of present errors is within a predefined range of thenumber of errors that are correctable by a Reed-Solomon error correctionalgorithm
 9. The memory sled of claim 7, wherein to determine whethermore than a number of errors that are correctable by an error correctionalgorithm are present in the read data comprises to determine whetherthe number of present errors is within a predefined range of the numberof errors that are correctable by a low-density parity check errorcorrection algorithm.
 10. The memory sled of claim 1, wherein the remotecompute sled is one of a plurality of remote compute sleds to whichbyte-addressable memory from the memory pool has been allocated and thememory pool controller is further to compare an address of the faultymemory region to an address space mapped to each remote compute sled,and wherein to send the notification to the remote compute sledcomprises to send the notification to a remote compute sled that has amemory address space to which the faulty memory region is mapped. 11.The memory sled of claim 1, wherein the memory pool controller isfurther to perform, on the memory pool, a memory access operation forthe remote compute sled.
 12. The memory sled of claim 11, wherein toperform a memory access operation comprises to write, in response to amemory access request from the remote compute sled, data to the memorypool.
 13. One or more machine-readable storage media comprising aplurality of instructions stored thereon that, in response to beingexecuted, cause a memory sled to: write test data to a byte-addressablememory region in a memory pool, wherein the memory region is to beaccessed by a remote compute sled through a network; read data from thememory region to which the test data was written; compare the read datato the test data to determine whether a threshold number of errors arepresent in the read data; and send, in response to a determination thatthe threshold number of errors are present in the read data, anotification through the network that the memory region is faulty. 14.The one or more machine-readable storage media of claim 13, wherein theplurality of instructions further cause the memory sled to: receive,from an orchestrator server, an allocation request to allocatebyte-addressable memory from the memory pool to the remote compute sled;and send, in response to a determination that the threshold number oferrors are present in the read data, a second notification to theorchestrator server that the memory region is faulty.
 15. The one ormore machine-readable storage media of claim 13, wherein the pluralityof instructions further cause the memory sled to: produce address spacedata for the remote compute sled, wherein the address space data isindicative of addresses of the byte-addressable memory allocated to theremote compute sled; and send, to the remote compute sled, the addressspace data.
 16. The one or more machine-readable storage media of claim15, wherein to produce the address space data comprises to produce acontiguous address space from non-contiguous memory addresses in thememory pool.
 17. The one or more machine-readable storage media of claim13, wherein the plurality of instructions further cause the memory sledto: allocate, after the notification that the memory region is faultyhas been sent, a second memory region of the memory pool for use by theremote compute sled; and write, to the second memory region of thememory pool, an error-corrected version of the data from the faultymemory region.
 18. The one or more machine-readable storage media ofclaim 13, wherein the plurality of instructions further cause the memorysled to test all of the memory in the memory pool for faults.
 19. Theone or more machine-readable storage media of claim 13, wherein todetermine whether a threshold number of errors are present in the readdata comprises to determine whether the number of present errors iswithin a predefined range of the number of errors that are correctableby an error correction algorithm
 20. The one or more machine-readablestorage media of claim 19, wherein to determine whether more than anumber of errors that are correctable by an error correction algorithmare present in the read data comprises to determine whether the numberof present errors is within a predefined range of the number of errorsthat are correctable by a Reed-Solomon error correction algorithm 21.The one or more machine-readable storage media of claim 19, wherein todetermine whether more than a number of errors that are correctable byan error correction algorithm are present in the read data comprises todetermine whether the number of present errors is within a predefinedrange of the number of errors that are correctable by a low-densityparity check error correction algorithm.
 22. The one or moremachine-readable storage media of claim 13, wherein the remote computesled is one of a plurality of remote compute sleds to whichbyte-addressable memory from the memory pool has been allocated and theplurality of instructions further cause the memory sled to compare anaddress of the faulty memory region to an address space mapped to eachremote compute sled, and wherein to send the notification to the remotecompute sled comprises to send the notification to a remote compute sledthat has a memory address space to which the faulty memory region ismapped.
 23. A method comprising: writing, by a memory sled, test data toa byte-addressable memory region of a memory pool, wherein the memoryregion is to be accessed by a remote compute sled through a network;reading, by the memory sled, data from the memory region to which thetest data was written; comparing, by the memory sled, the read data tothe test data to determine whether a threshold number of errors arepresent in the read data; and sending, by the memory sled and inresponse to a determination that the threshold number of errors arepresent in the read data, a notification through the network that thememory region is faulty.
 24. The method of claim 23, further comprising:receiving, from an orchestrator server and by the memory sled, anallocation request to allocate byte-addressable memory from the memorypool to the remote compute sled; and sending, by the memory sled and inresponse to a determination that the threshold number of errors arepresent in the read data, a second notification to the orchestratorserver that the memory region is faulty.
 25. The method of claim 23,further comprising: producing, by the memory sled, address space datafor the remote compute sled, wherein the address space data isindicative of addresses of the byte-addressable memory allocated to theremote compute sled; and sending, by the memory sled, the address spacedata to the remote compute sled.